Adaptive fairness algorithm delivers near-perfect energy sharing in peer-to-peer microgrids

As rooftop solar panels, home batteries and electric vehicle chargers turn ordinary consumers into energy traders, a quiet mathematical problem has become one of the defining challenges of the decarbonized grid: when several buyers compete for a limited pool of surplus solar power, who gets what? A new study published in Results in Engineering by A.V. Sreekumar and R.R. Lekshmi tackles this question head-on, evaluating six energy allocation strategies for peer-to-peer microgrids and proposing an adaptive framework that empirically achieves a fairness index of 0.9995 over a full simulated year—while completing each allocation decision in microseconds on an ordinary desktop computer.

The research emerges from a structural shift in power systems. International climate accords have driven national pledges to replace fossil-fuel infrastructure with renewables, and distributed energy architectures are replacing centralized utility models. Placing generation close to consumption reduces transmission losses and enhances grid resilience, while commercial entities and residential consumers become active market participants through rooftop photovoltaic installations, battery energy storage systems and small-scale wind turbines. Electric vehicle charging stations equipped with solar arrays can even bid strategically into electricity markets. In this landscape, peer-to-peer trading allows prosumers to exchange energy directly, bypassing conventional utility transactions—but it also raises questions about benefit sharing that economic efficiency alone cannot answer.

Those questions belong to the framework of energy justice, which analyzes fairness in the energy sector across distributive, recognition and procedural dimensions. As distributed generation penetrates deeper into grids, distributive justice—how benefits and burdens are shared among participants—becomes increasingly prominent. The authors note that unfavorable tariff structures and complicated interconnection requirements substantially reduce the economic feasibility of residential solar installations, and prosumers are often paid significantly less for grid exports through feed-in tariffs, creating a pricing disparity that deters investment in distributed energy resources. A well-designed decentralized market, they argue, can mitigate these barriers and foster a more equitable environment for small-scale renewable investment.

The study’s central contribution is the Adaptive Fairness-Based Allocation (AFBA) framework, which embeds fairness as a core objective rather than a post-hoc adjustment. The method works at the market layer of a five-node community microgrid, where a solar-equipped prosumer shares surplus energy with three buyers: an industrial consumer with a 300 kWh demand cap bidding ₹5.90/kWh, a commercial EV charging station capped at 250 kWh bidding ₹7.15/kWh, and a domestic consumer capped at 200 kWh bidding ₹5.58/kWh. Surplus energy is structured into six four-hour slots per day, and any energy left after all buyers are served is exported to the grid at a feed-in tariff of ₹4.5/kWh.

Technically, AFBA computes each buyer’s participation factor as the ratio of its weighted bid to the total weighted bids of all buyers, with weights updated adaptively from historical allocation outcomes. After every surplus slot, the framework stores one cumulative deviation per buyer—the gap between that buyer’s actual allocation and the equal-share level—using exponential memory decay with a factor of 0.95, so shortfalls in one slot can be corrected in subsequent slots. A bounded nonlinear mapping prevents stored deviations from producing oversized weight jumps, while a learning rate of 0.1, a compensation factor of 0.2 providing momentum, a nonlinearity factor of 1.2, and a smoothing factor of 0.5 stabilize the update. A minimum weight constraint of 0.1 ensures no buyer is ever completely excluded, and weights are normalized to sum to unity before each allocation.

The authors benchmarked AFBA against five alternatives: fixed contractual allocation, priority-first allocation with proportional redistribution, a price-weighted fairness method, linear programming (LP), and a genetic algorithm (GA). LP maximizes slot revenue subject to power balance and demand constraints and serves as the revenue benchmark; GA searches for near-optimal solutions stochastically, with results averaged over 20 independent runs. The evaluation used real-world data: solar generation from a private plant in Kuzhikattussery, Thrissur, Kerala, and EV charging demand from Perth & Kinross Council’s charging stations in Perth, UK, covering 2024 at 15-minute intervals, aggregated into 2,190 calendar slots of which 646 were surplus slots totaling 154,309.89 kWh of available energy.

The results reveal a stark trade-off. Under fixed pricing, LP achieved the highest annual revenue of ₹10.49 lakhs but the second-worst fairness at 0.4409, concentrating 72.74 percent of annual energy on the highest bidder while the domestic buyer received just 1.8 percent. The priority-first method fared even worse on fairness at 0.4162, starving the lowest-bid buyer to 3.98 percent of yearly allocation. AFBA, by contrast, delivered almost perfectly balanced yearly allocations of 33.33, 33.27 and 33.13 percent, a fairness index of 0.9995, value fairness of 0.9877, and revenue of ₹9.58 lakhs—only 9.0 percent below the LP benchmark. The price-weighted method offered the best revenue among fair-oriented approaches at ₹9.69 lakhs with fairness of 0.9882, while GA reached 0.9935 fairness but required roughly 28.6 milliseconds per slot on average, far too slow for time-critical clearing.

Computation time proved a decisive differentiator. AFBA completed each allocation in 0.0255 milliseconds under fixed pricing and 0.1467 milliseconds under time-of-day (ToD) pricing, while the price-weighted method ran in 0.0093 to 0.0183 milliseconds. LP required around 1.18 milliseconds and GA between 24.5 and 28.6 milliseconds. The authors also tested AFBA’s resilience under ToD tariffs derived from Kerala State Electricity Regulatory Commission regulations and Ministry of Power guidelines, applying multipliers that discount bids by 20 percent during solar hours and surcharge them by 10 percent at night. Fairness performance remained essentially unchanged under dynamic pricing, and the fairness progression analysis showed AFBA improving by roughly +0.4 percent over successive surplus slots, confirming that the system genuinely learns from experience—a property absent in static or optimization-only methods.

Scalability testing reinforced the practical promise. When the number of buyers was increased from 3 to 100, average fairness rose from 0.931 to 0.984 and stabilized, while per-slot computation time stayed between 0.087 and 0.192 milliseconds. On the same machine, LP took about 1.18 milliseconds and GA about 31 milliseconds per slot with three buyers. The authors acknowledge limitations: the allocation operates only at the market layer, ignoring voltage limits, line ratings and power-flow equations, and the generation and demand profiles come from two independent regional datasets rather than a physically co-located microgrid. Equal energy allocation, they note, is also inappropriate when buyers have contracts of different grades, non-deferrable loads, or significantly varying demand constraints—in such cases a fill-rate criterion or priority rule serves better.

The study’s implications reach beyond the test system. Existing research had achieved at most a Jain fairness index of 0.94, lacked frameworks to track cumulative fairness deviations with memory decay, and had not integrated ToD tariffs for distribution fairness under real regulatory frameworks. By raising the yearly index to 0.9995 at the cost of an 8.8 to 9.0 percent revenue gap relative to unconstrained revenue maximization, AFBA quantifies precisely what equity costs in a local energy market—and shows that the price is modest. For community microgrids deciding how to split their solar surplus, the message is clear: long-term equality among participants is achievable in microseconds, and markets that prioritize it may retain the trust and participation needed to sustain the decentralized energy transition.

Subject of Research: Energy

Article Title: Achieving equitable and efficient energy trading in peer-to-peer microgrids: An evaluation of allocation strategies

Article References: Sreekumar, A. V., & Lekshmi, R. R. (2026). Achieving equitable and efficient energy trading in peer-to-peer microgrids: An evaluation of allocation strategies. Results in Engineering, 32, Article 113537. https://doi.org/10.1016/j.rineng.2026.113537

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113537

Keywords: peer-to-peer energy trading, microgrids, energy justice, fairness algorithms, solar power, electric vehicles, distributed energy resources, Achieving, equitable, efficient, energy, trading